Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation Read chapter Read first chapter

2018 | OriginalPaper | Chapter

Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework

Authors : Guoqing Wu, Yuanyuan Wang, Jinhua Yu

Published in: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Accurate prognosis for high grade glioma (HGG) is of great clinical value since it would provide optimized guidelines for treatment planning. Previous imaging-based survival prediction generally relies on some features guided by clinical experiences, which limits the full utilization of biomedical image. In this paper, we propose a sparse representation-based radiomics framework to predict overall survival (OS) time of HGG. Firstly, we develop a patch-based sparse representation method to extract the high-throughput tumor texture features. Then, we propose to combine locality preserving projection and sparse representation to select discriminating features. Finally, we treat the OS time prediction as a classification task and apply sparse representation to classification. Experiment results show that, with 10-fold cross-validation, the proposed method achieves the accuracy of 94.83% and 95.69% by using T1 contrast-enhanced and T2 weighted magnetic resonance images, respectively.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Generalised Wasserstein Dice Score for Imbalanced Multi-class Segmentation Using Holistic Convolutional Networks
next chapter Traumatic Brain Lesion Quantification Based on Mean Diffusivity Changes
Literature
1.
Pope, W.B., et al.: MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am. J. Neuroradiol. 26(10), 2466–2474 (2005)
2.
Gutman, D.A., et al.: MR imaging predictors of molecular profile and survival: multi-institutional study of the TCGA glioblastoma data set. Radiology 267(2), 560–569 (2013)CrossRef
3.
Macyszyn, L., et al.: Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-Oncology 18(3), 417–425 (2016)CrossRef
4.
Zacharaki, E.I., et al.: Survival analysis of patients with high-grade gliomas based on data mining of imaging variables. Am. J. Neuroradiol. 33(6), 1065–1071 (2012)CrossRef
5.
Pillai, J.J., Zacá, D.: Clinical utility of cerebrovascular reactivity mapping in patients with low grade gliomas (2011)
6.
Aerts, H.J.: The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2(12), 1636–1642 (2016)CrossRef
7.
Prasanna, P., et al.: Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur. Radiol., 1–10 (2016)
8.
Zhang, H., et al.: SU-F-R-04: radiomics for survival prediction in glioblastoma (GBM). Med. Phys. 43(6), 3373 (2016)CrossRef
9.
Liu, L., Zhang, H., Rekik, I., Chen, X., Wang, Q., Shen, D.: Outcome prediction for patient with high-grade gliomas from brain functional and structural networks. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 26–34. Springer, Cham (2016). https://​doi.​org/​10.​1007/​978-3-319-46723-8_​4 CrossRef
10.
Dong, W., et al.: Image reconstruction with locally adaptive sparsity and nonlocal robust regularization. Signal Process-Image. Commun. 27(10), 1109–1122 (2012)CrossRef
11.
Zhu, X., et al.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1263–1275 (2017)MathSciNetCrossRef
12.
Lin, D., et al.: Sparse models for correlative and integrative analysis of imaging and genetic data. J. Neurosci. Meth. 237, 69–78 (2014)CrossRef
13.
Lin, D., et al.: Correspondence between fMRI and SNP data by group sparse canonical correlation analysis. Med. Image Anal. 18(6), 891–902 (2016)CrossRef
14.
Vounou, M., et al.: Discovering genetic associations with high-dimensional neuroimaging phenotypes: a sparse reduced-rank regression approach. Neuroimage 53(3), 1147–1159 (2010)CrossRef
15.
Wright, J., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRef
16.
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data (2017, in press)
17.
18.
19.
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)CrossRef
20.
Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRef
21.
Zhu, X., et al.: Subspace regularized sparse multi-task learning for multi-class neurodegenerative disease identification. IEEE Trans. Biomed. Eng. 63(3), 607–618 (2016)CrossRef
22.
Liu, M., Zhang, D.: Feature selection with effective distance. Neurocomputing 215, 100–109 (2016)CrossRef
Metadata
Title
Overall Survival Time Prediction for High Grade Gliomas Based on Sparse Representation Framework
Authors
Guoqing Wu
Yuanyuan Wang
Jinhua Yu
Copyright Year
2018
Book
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

Print ISBN: 978-3-319-75237-2

Electronic ISBN: 978-3-319-75238-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-75238-9_7

Premium Partner

    Image Credits